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land Article Development of Flood Damage Regression Models by Rainfall Identification Reflecting Landscape Features in Gangwon Province, the Republic of Korea Hyun Il Choi Department of Civil Engineering, Yeungnam University, 280 Daehak-Ro, Gyeongsan 38541, Gyeongbuk, Korea; [email protected]; Tel.: +82-53-810-2413 Abstract: Torrential rainfall events associated with rainstorms and typhoons are the main causes of flood-related economic losses in Gangwon Province, Republic of Korea. The frequency and severity of flood damage have been increasing due to frequent extreme rainfall events as a result of climate change. Rainfall is a major cause of flood damage for the study site, given a strong relationship between the probability of flood damage over the last two decades and the maximum rainfall for 6 and 24 h durations in the 18 administrative districts of Gangwon Province. This study aims to develop flood damage regression models by rainfall identification for use in a simplified and efficient assessment of flood damage risk in ungauged or poorly gauged regions. Optimal simple regression models were selected from four types of non-linear functions with one of five composite predictors averaged for the two rainfall datasets. To identify appropriate predictor rainfall variables indicative of regional landscape features, the relationships between the composite rainfall predictor and landscape characteristics such as district size, topographic features, and urbanization rate were interpreted. The proposed optimal regression models may provide governments and policymakers with an efficient flood damage risk map simply using a regression outcome to design or forecast rainfall data. Citation: Choi, H.I. Development of Keywords: flood damage; rainfall; landscape; simple regression; damage risk map Flood Damage Regression Models by Rainfall Identification Reflecting Landscape Features in Gangwon Province, the Republic of Korea. Land 1. Introduction 2021 10 , , 123. https://doi.org/ Global warming and climate change have increased the frequency and severity of 10.3390/land10020123 extreme weather events, which has in turn elevated the risk of severe climate-related natural disasters [1–3]. Natural disasters may directly incur substantial human and economic Received: 20 December 2020 damage costs, and flood-related disasters are one of the most frequent and deadliest Accepted: 26 January 2021 Published: 27 January 2021 natural disasters worldwide [4]. The Korean Peninsula annually experiences flood damage by the East Asian monsoon, and the flood damage costs caused by rainstorms and typhoons Publisher’s Note: MDPI stays neutral account for the majority of damage losses caused by natural disasters in the Republic of with regard to jurisdictional claims in Korea [5]. Climate changes may also have a greater influence on extreme rainfall patterns published maps and institutional affil- in Gangwon Province than in other regions of the Korean Peninsula. This is related to the iations. complex geographical landscape of the province associated with the Taebaek Mountain Range and the East Sea. These features divide the province into the western region with a mountainous climate and the eastern region with an oceanic climate. In terms of the historic extreme events, Gangneung City in the eastern province received the highest recorded daily rainfall of 880 mm. This was considered a 200-year event, due to a localized downpour Copyright: © 2021 by the author. Licensee MDPI, Basel, Switzerland. from severe thunderstorms by Typhoon Rusa on 31 August 2002 [5,6]. On 29 August 2018, This article is an open access article Cheorwon County in the western province recorded the heaviest downpour, measuring distributed under the terms and 113.5 mm/h with a return period exceeding 500 years, due to a localized stagnant front conditions of the Creative Commons created between the cold air mass from the northwest and the hot and humid air mass Attribution (CC BY) license (https:// from the East Sea [5]. A number of severe localized downpours associated with torrential creativecommons.org/licenses/by/ rainstorms and super typhoons frequently occur because of the mountainous and coastal 4.0/). landscapes characteristic to Gangwon Province. The major countermeasures against the Land 2021, 10, 123. https://doi.org/10.3390/land10020123 https://www.mdpi.com/journal/land Land 2021, 10, 123 2 of 14 flood damage have focused on supporting recovery costs for flood damaged areas in the Republic of Korea [5]. As such, preemptive flood management measures are required to reduce the human and economic damage costs from recent flood disasters. Assessment of the vulnerability or risk to regional flood hazard is one of the non-structural measures to prepare integrated mitigation measures customized to regional flood damage [7,8]. For proactive approaches to flood risk management strategies, there is a need for a method that can predict future flood damage risk by analyzing the characteristics and trends of regional flood damage records [9]. Flood damage risk or vulnerability assessments should be based on flood hazard and inundation analysis results using hydrologic and hydraulic models. However, the lack of available hydrological data and information of a decent quality introduces a degree of uncertainty in validating model simulation results, particularly the case for ungauged regions. The lack of reliable data is a crucial barrier to flood damage analysis and flood risk assessment [10]. To resolve these issues, regression analysis presents itself as an alter- native method that may be an effective tool in predicting hydrological variables through acceptable relationships with influencers to overcome limited hydrological data in spatial and temporal resolutions on target regions to be analyzed [11]. Many studies have shown that rainfall characteristics have a significant impact on flood damage events from complex influencing factors [12–19]. Elucidation of a functional relationship between rainfall and flood damage could relate the amount of flood damage or flood events to rainfall conditions. As such, the risk of flood damage may also be estimated by determining the rainfall–flood damage relationship through regression analysis [12,15,17,19]. Most previous studies have conducted regression analysis using the fixed predictor rainfall variables in a single regres- sion function to develop regional damage regression models. However, the variations in flood damage attributable to rainfall were not high in some rainfall-flood damage regres- sion models. To improve the prediction performance of rainfall-flood damage regression analysis, it is necessary to identify rainfall variables that reflect regional characteristics; these typically have a non-linear relationship with the features of flood damage. The aim of this study is to provide a methodology to develop rainfall-flood damage regression models for assessing the relative flood damage risk associated with a specific amount of rainfall for designing or forecasting purposes. This paper proposes optimal regression models to estimate regional flood damage. These models were selected from four types of regression functions, with one of the five predictor rainfall variables capable of representing the regional landscape and terrain features. The proposed methodology was implemented through various regression analysis models for Gangwon Province, Republic of Korea. This area characterized by a complex landscape of mountainous and coastal areas and lacks in available and/or reliable hydrological data. Flood damage data caused by rainstorms and typhoons were collected from annual disaster reports [5], provided by the Ministry of the Interior and Safety for the last 20 years from 1999 to 2018. The analysis period over the last two decades was determined by comprehensively considering the amount of data necessary for regression analysis and the consistency in damage features of past data for the study area. Rainfall data were collected from 16 automated surface observing system (ASOS) meteorological stations [20], managed by the Korea Meteorological Agency around the 18 administrative districts of Gangwon Province. The ASOS meteorological gauge stations undertake continuous measurements of hourly rainfall observations for the analysis period of flood damage records. Although there are no generalized guidelines for sample size requirements appropriate for regression analysis, this study has adopted one of the various rules-of-thumb that recommends at least 10 cases per variable [21–23]. Therefore, several non-linear functions were applied to a simple regression analysis with a single composite predictor averaged by different rainfall characteristics. This accounted for the minimum number of 12 damage records for the study site. The identification of a suitable predictor rainfall variable that incorporates regional landscape features may improve the possible applications of rainfall–flood damage regression results. Land 2021, 10, 123 3 of 14 2. Materials and Methods 2.1. Study Region Figure1 shows that the Gangwon Province is located between 37 ◦020 N–38◦370 N and 127◦050 E–129◦220 E in the mid-eastern part of the Korean Peninsula. It is located at the eastern end of the Asian continent bordered by the East Sea, a margin of the Western Pacific Ocean. The Gangwon Province comprises 18 administrative districts (7 cities and 11 counties), spanning an area of 16,874 km2; this makes up 16.8% of the national territory of the Republic of Korea. Figure2a illustrates that the landscape is dominated

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